This proposal addresses the difficulty of using observational data to drawing causal inferences on treatment effects. Methodological advances in this area have enormous implications for efforts to identify the most promising leverage points for interventions and thereby optimize clinical advice regarding a range of interventions or treatments affecting behavioral, biomarker, or psychosocial risk factors. Our proposal develops prior work on Mendelian Randomization (MR) designs, which are a special case of instrumental variables that use genetic variants as instruments. We extend and strengthen MR applications by taking advantage of data already collected in the context of Genome Wide Association Studies (GWAS). Embedding MR studies in GWAS data will result in more powerful studies and better assessments of the validity of those studies, leading to more credible MR effect estimates. We propose capitalizing on the GWAS data to allow four important innovations in MR studies. First, GWAS allows comprehensive characterization of background genetic characteristics to control for possible bias due to population stratification. Second, information on multiple candidate genetic instruments can be used to create multi-gene risk scores. These will provide far stronger instruments than single genes alone, improving both the statistical power and the opportunities to critically evaluate the assumptions for a valid MR study. Third, we can take advantage of the multiple instruments to conduct over-identification tests for the validity of each instrument. Over-identification tests are a standard econometric tool for evaluating instrumental variables but they have to date not been applied in MR studies. Finally, we demonstrate a novel approach to evaluating the assumptions of MR using gene- environment and gene-gene interactions. We use as an example current research on the effects of psychosocial phenotypes (symptoms of depression and anxiety and social integration) on diabetes and coronary heart disease. Psychosocial effects on both diseases are well-supported in observational studies, but previous randomized trials intervening on psychosocial risk factors had disappointing results. Thus, this is an ideal area in which to implement new approaches to analyzing observational data. Our analyses are based on previously collected GWAS data in two cohorts, the Nurses'Health Study and the Health Professionals Follow-up Study. We have identified a number of candidate genes likely to influence each of the psychosocial phenotypes and we will also take advantage of GWAS studies for the phenotypes of interest to calculate multi- gene or polygenic risk scores to use as instruments in MR studies. MR can be an extremely powerful tool to estimate causal effects, and applications of this approach have increased very rapidly. However, MR rests on strong assumptions that are rarely critically tested;in part this is because tools to evaluate these assumptions have not been available. Our work is intended to address this gap and foster judicious applications of MR to provide credible effect estimates in observational data. PUBLIC HEALTH RELEVANCE: Mendelian Randomization is a method that may offer new opportunities to derive credible effect estimates from observational data. This method uses genotypes that influence exposure to a particular biomarker, behavior, or other type of risk factor. Variations in this genotype may provide natural experiments to estimate the effect of the risk factor on subsequent health outcomes. We use new tools to make this study design stronger and to evaluate whether the effect estimates from this design are unbiased. Our methods take advantage of large investments already made by many epidemiologic samples in collecting data for genome wide association studies. To illustrate the methods, we focus on estimating the effects of psychosocial distress and social integration on coronary heart disease and diabetes.